MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.

Package details

AuthorKeefe Murphy [aut, cre] (<>), Thomas Brendan Murphy [ctb] (<>)
MaintainerKeefe Murphy <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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MoEClust documentation built on Jan. 6, 2021, 5:10 p.m.